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| import streamlit as st | |
| import os | |
| import time | |
| from dotenv import load_dotenv | |
| from getpass import getpass | |
| from langchain.llms import replicate | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| from langchain.prompts import PromptTemplate | |
| from PyPDF2 import PdfReader | |
| from streamlit_extras.add_vertical_space import add_vertical_space | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| #from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import faiss | |
| load_dotenv() | |
| REPLICATE_API_TOKEN = os.environ.get("REPLICATE_API_TOKEN") | |
| with st.sidebar: | |
| st.title("File Research using LLM") | |
| st.markdown(''' Upload your file and ask questions and do Research''') | |
| add_vertical_space(5) | |
| pdf=st.file_uploader('Upload your file (PDF)', type='pdf') | |
| if pdf is not None: | |
| pdf_reader=PdfReader(pdf) | |
| text="" | |
| for page in pdf_reader.pages: | |
| text+=page.extract_text() | |
| text_splitter=RecursiveCharacterTextSplitter( | |
| chunk_size=1000, | |
| chunk_overlap=200, | |
| length_function=len | |
| ) | |
| chunks=text_splitter.split_text(text) | |
| st.write('Made by ALOK') | |
| def main(): | |
| st.header('Talk to your file') | |
| os.environ["REPLICATE_API_TOKEN"]=REPLICATE_API_TOKEN | |
| #embeddings=OpenAIEmbeddings() | |
| #vectorstore=faiss.FAISS.from_texts(chunks, embedding=embeddings) | |
| # The meta/llama-2-70b-chat model can stream output as it's running. | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # React to user input | |
| if prompt := st.chat_input("Type Here"): | |
| # Display user message in chat message container | |
| st.chat_message("user").markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| replite_api='r8_4fktoXrDGkgHY8uw1XlVtQJKQlAILKv0iBmPI' | |
| # rep = replicate.Client(api_token=replite_api) | |
| # output = replicate.run( | |
| # "meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", | |
| # input={"prompt": prompt} | |
| # ) | |
| model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3" | |
| llm=replicate.Replicate( | |
| streaming=True, | |
| callbacks=[StreamingStdOutCallbackHandler()], | |
| model=model, | |
| model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1}, | |
| replicate_api_token=REPLICATE_API_TOKEN | |
| ) | |
| prompt = """ | |
| User: Answer the following yes/no question by reasoning step by step. Please don't provide incomplete answer. Can a dog drive a car? | |
| Assistant: | |
| """ | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| message_placeholder.markdown(llm(prompt) + "▌") | |
| # # The predict method returns an iterator, and you can iterate over that output. | |
| # response_till_now='' | |
| # for item in output: | |
| # response_till_now+=item | |
| # time.sleep(0.03) | |
| # message_placeholder.markdown(response_till_now + "▌") | |
| # message_placeholder.markdown(response_till_now) | |
| # response = f"AI: {response_till_now}" | |
| # Add assistant response to chat history | |
| # st.session_state.messages.append({"role": "assistant", "content": response}) | |
| # https://replicate.com/meta/llama-2-70b-chat/versions/02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3/api#output-schema | |
| #print(item, end="") | |
| if __name__=='__main__': | |
| main() |